The road to production has become increasingly harder. Machine Learning Engineers who turn prototypes into production-ready software face difficulties with the lack of tooling and best-practices. spaCy v3, with its configuration and project system, introduced a way to solve this problem. Here's my take on how it works, and how it can ramp-up your team!
LLMs are not a direct solution to most of the NLP use-cases companies have been working on. They are extremely useful, but if you want to deliver reliable software you can improve over time, you can't just write a prompt and call it a day. Once you're past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability.
Developer productivity has been central to our design of spaCy, both in smaller decisions and some of the bigger architectural questions. We believe in embracing the complexities of machine learning, not hiding it away under leaky abstractions, while also maintaining the developer experience. Read on to learn some of the design patterns within the library, how we've implemented them, and most importantly, why.
“Commercial machine learning projects are currently like start-ups: many projects fail, but some are extremely successful, justifying the total investment. While some people will tell you to embrace failure, I say failure sucks — so what can we do to fight it? In this talk, I will discuss how to address some of the most likely causes of failure for new NLP projects.”
Many people assume that working on an NLP project involves a lot of machine learning. Our experience is that it's much less about flowing tensors, and more about making a tailored solution. This blogposts demonstrates how a typical spaCy project could be initiated, implemented and executed towards a custom solution.
We’ve been running Explosion for about five years now, which has given us a lot of insights into what Natural Language Processing looks like in industry contexts. In this blog post, I’m going to discuss some of the biggest challenges for applied NLP and translating business problems into machine learning solutions.
“What can you do to maximize probability of success for your Machine Learning solution? Throughout my 15 years as data scientist in academia, big pharma and through consulting, one common theme has emerged: the most reliable predictor of success for any NLP or ML-based solution is whether or not you involve the data science team early on.”